Road traffic accident causation and ITS: how do we choose the most effective solutions?

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1 Institute for Transport Studies Road traffic accident causation and ITS: how do we choose the most effective solutions? Oliver Carsten Institute for Transport Studies University of Leeds UK

2 How do we learn about road safety problems and solutions? Typical alternatives are to: 1. Learn from accidents 2. Look at current technologies and see how they might assist road users

3 Learning from accidents We can look at accidents and identify clusters Thus Carsten and Draskóczy, 1995 drew on previous work (e.g. Hydén and Draskóczy, 1992) to identify the major safety problems in Europe as: single vehicle accidents on motorways rear-end collisions on motorways single vehicle accidents on rural roads head-on collisions on rural roads crossing collisions at rural intersections crossing collisions at urban intersections pedestrian and bicyclist accidents in urban areas

4 Driver type We can also observe the role of individual factors in traffic accidents: Young drivers are over-involved in loss of control crashes Elderly drivers are over-involved in intersection crashes

5 One can also look at accident contributory factors Leeds in-depth study: Urban Accidents: Why do they Happen? (1990)

6 Study aim To look at the causation of urban road accidents with particular emphasis on the role of human factors Study cases: 1254 accidents in north Leeds in 1988 Total of 2454 immediate participants

7 Contributory factors Multi-level scheme from immediate road user errors at top to more remote causes at bottom Based on standard of a reasonable road user Assigned to each participant in a case conference A chain of contributory factors for a driver might be: failure to yield - minor into major caused by: failed to look at all caused by: impairment alcohol and fatigue

8 Driver and rider factors At the top (immediate) level, the most common failures for non-innocent drivers and riders were: Failure to yield (16%) Loss of control (7%) Manoeuvre problems (mainly inappropriate overtaking) (4%) Failure to stop (2%)

9 Driver and rider factors (2) At the main explanatory level (the third level) the most common explanations of driver and rider failures were: Perceptual error 16% Unable to see 12% Cognitive (judgement) error 12% Lack of skills 3% Attitude problem 2%

10 Behaviours increasing accident risk At the second level, which examined behaviours that increased the risk of an accident, there were some interesting differences by age and sex: Driving too fast for the situation was more commonly coded for younger drivers and much more so for males (but only coded for 5% of drivers and riders) Following too close was less common for older drivers (40+)

11 Accident scenarios One can also combine the accident types (e.g. urban intersection) with the contributory factors to identify scenarios

12 Two example scenarios from Fleury and Brenac (2001)

13 The sharp end and the blunt end Humans make errors; systems reduce errors and protect against the consequences of error Sharp end (individuals) Blunt end (systems) Successive layers of defences, barriers and safeguards The latest version of the Swiss cheese model (Reason, 2008)

14 Road traffic compared to aviation or process plants Blunt end Sharp end In road traffic, individuals have many degrees of freedom and the role of individual compliance and error is thus much more important

15 But we do also implement solutions at the blunt end Such solutions eliminate whole categories of crashes Example is speed management

16 Another solution at the blunt end : the Swedish 2+1 road design

17 The crash sequence (Tingvall, 2006)

18 So maybe we can infer that Systems that increase driver compliance (e.g. Intelligent Speed Adaptation (ISA0, alcolocks, etc.) will have wider impacts than systems that focus on particular errors (e.g. FCW, LDW) EuroFOT estimates that FCW reduces injury accidents by approx. 4% on motorways ISA-UK predicts up to 29% reduction in injury accidents with ISA Where a crash is imminent, automated intervention such as AEB can contribute (= a final system defence with the possibility of human error being eliminated) We perhaps need to go back to first principles and create systems that really support the driver

19 ITERATE based on Carsten (2007) Goal: keep risk of violation and error low and maintain safety margins

20 Conclusions Systems that prevent deviation from normal (rule-compliant) driving will tend to be more effective than systems that support drivers nearer to a crash event There is a role for systems that take over from the driver when a crash is imminent For driver support, we need to focus on driver state and managing task demand to prevent overload and underload We need to be wary of technology for technology s sake

21 Thank you for your attention! B

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